Description: A Dynamically Pruned Network is a type of neural network that optimizes its architecture during the training process by eliminating connections that are deemed less important. This approach allows the network to adapt and become more efficient, reducing model complexity without sacrificing performance. Dynamic pruning is based on the idea that not all neural connections are equally relevant to the task being learned; some may be redundant or unnecessary. By removing these connections, the network not only becomes lighter but can also improve its generalization ability, meaning it can perform better on unseen data. This process is carried out iteratively, where the network continuously evaluates the importance of each connection and adjusts its structure accordingly. Dynamically pruned networks are particularly useful in environments where computational resources are limited, as they allow for high performance with reduced memory and processing power usage. Additionally, this approach can facilitate the training of larger and more complex models by focusing on the most relevant connections, thus optimizing training time and overall model efficiency.
